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1. Nanoantennas with balanced gain and loss

   https://doi.org/10.1515/nanoph-2019-0392  

   Sanders, Stephen; Manjavacas, Alejandro (February 2020, Nanophotonics)

   Abstract The large cross sections and strong confinement provided by the plasmon resonances of metallic nanostructures make these systems an ideal platform to implement nanoantennas. Like their macroscopic counterparts, nanoantennas enhance the coupling between deep subwavelength emitters and free radiation, providing, at the same time, an increased directionality. Here, inspired by the recent works in parity-time symmetric plasmonics, we investigate how the combination of conventional plasmonic nanostructures with active materials, which display optical gain when externally pumped, can serve to enhance the performance of metallic nanoantennas. We find that the presence of gain, in addition to mitigating the losses and therefore increasing the power radiated or absorbed by an emitter, introduces a phase difference between the elements of the nanoantenna that makes the optical response of the system directional, even in the absence of geometrical asymmetry. Exploiting these properties, we analyse how a pair of nanoantennas with balanced gain and loss can enhance the far-field interaction between two dipole emitters. The results of this work provide valuable insight into the optical response of nanoantennas made of active and passive plasmonic nanostructures, with potential applications for the design of optical devices capable of actively controlling light at the nanoscale. 

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2. Loss Functions, Axioms, and Peer Review

   https://doi.org/10.1613/jair.1.12554  

   Noothigattu, Ritesh; Shah, Nihar; Procaccia, Ariel (January 2021, Journal of Artificial Intelligence Research)

   It is common to see a handful of reviewers reject a highly novel paper, because they view, say, extensive experiments as far more important than novelty, whereas the community as a whole would have embraced the paper. More generally, the disparate mapping of criteria scores to final recommendations by different reviewers is a major source of inconsistency in peer review. In this paper we present a framework inspired by empirical risk minimization (ERM) for learning the community's aggregate mapping. The key challenge that arises is the specification of a loss function for ERM. We consider the class of L(p,q) loss functions, which is a matrix-extension of the standard class of Lp losses on vectors; here the choice of the loss function amounts to choosing the hyperparameters p and q. To deal with the absence of ground truth in our problem, we instead draw on computational social choice to identify desirable values of the hyperparameters p and q. Specifically, we characterize p=q=1 as the only choice of these hyperparameters that satisfies three natural axiomatic properties. Finally, we implement and apply our approach to reviews from IJCAI 2017. 

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3. Loss Functions, Axioms, and Peer Review

   Noothigattu, Ritesh; Shah, Nihar B.; Procaccia, Ariel D. (January 2021, Journal of artificial intelligence research)
4. Loss Functions, Axioms, and Peer Review

   Noothigattu, R; Shah, N; Procaccia, A (January 2021, Journal of artificial intelligence research)
5. Ordinal Hyperplane Loss

   https://doi.org/10.1109/BigData.2018.8622079  

   Vanderheyden, Bob; Xie, Ying (December 2018, 2018 IEEE International Conference on Big Data (Big Data))

   The problem of ordinal classification occurs in a large and growing number of areas. Some of the most common source and applications of ordinal data include rating scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity, facial age estimation, etc. The problem of predicting ordinal classes is typically addressed by either performing n-1 binary classification for n ordinal classes or treating ordinal classes as continuous values for regression. However, the first strategy doesn't fully utilize the ordering information of classes and the second strategy imposes a strong continuous assumption to ordinal classes. In this paper, we propose a novel loss function called Ordinal Hyperplane Loss (OHPL) that is particularly designed for data with ordinal classes. The proposal of OHPL is a significant advancement in predicting ordinal class data, since it enables deep learning techniques to be applied to the ordinal classification problem on both structured and unstructured data. By minimizing OHPL, a deep neural network learns to map data to an optimal space where the distance between points and their class centroids are minimized while a nontrivial ordinal relationship among classes are maintained. Experimental results show that deep neural network with OHPL not only outperforms the state-of-the-art alternatives on classification accuracy but also scales well to large ordinal classification problems. 

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